Multiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results

TL;DR

This study introduces a multiparametric radiomic framework that leverages various MRI modalities to accurately classify brain tumor grades and assess treatment response, showing high sensitivity, specificity, and potential as a biomarker.

Contribution

The paper presents a novel multiparametric radiomic approach that extends texture analysis to multiple MRI images for improved tumor grading and treatment response assessment in brain cancer.

Findings

Classified tumor grades with 93% sensitivity and 100% specificity.

Achieved an AUC of 0.95 for tumor grade classification.

Distinguished true from pseudo-progression with an AUC of 0.93.

Abstract

Radiomics is an exciting new area of texture research for extracting quantitative and morphological characteristics of pathological tissue. However, to date, only single images have been used for texture analysis. We have extended radiomic texture methods to use multiparametric (mp) data to get more complete information from all the images. These mpRadiomic methods could potentially provide a platform for stratification of tumor grade as well as assessment of treatment response in brain tumors. In brain, multiparametric MRI (mpMRI) are based on contrast enhanced T1-weighted imaging (T1WI), T2WI, Fluid Attenuated Inversion Recovery (FLAIR), Diffusion Weighted Imaging (DWI) and Perfusion Weighted Imaging (PWI). Therefore, we applied our multiparametric radiomic framework (mpRadiomic) on 24 patients with brain tumors (8 grade II and 16 grade IV). The mpRadiomic framework classified grade IV tumors from grade II tumors with a sensitivity and specificity of 93% and 100%, respectively, with an AUC of 0.95. For treatment response, the mpRadiomic framework classified pseudo-progression from true-progression with an AUC of 0.93. In conclusion, the mpRadiomic analysis was able to effectively capture the multiparametric brain MRI texture and could be used as potential biomarkers for distinguishing grade IV from grade II tumors as well as determining true-progression from pseudo-progression.